import numpy as np 
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from joblib import load
import tensorflow as tf

class NN(object):

    def __init__(self):
        self.lodaed_model = tf.keras.models.lodaed_model('timing/my_model.keras ')
        self.scaler = load('timing/scaler.joblib')
    
    def get_hourly_trend(self):
        """获取预测结果
        """
        n_steps = 7
        df_pivot = pd.read_csv('./timing/scenic_data.csv')
        # latest_data = df_pivot.iloc[-n_steps:].values #取后七天数据
        x_values = df_pivot.iloc[-n_steps:]
        x_values.iloc[-1,x_values.columns.get_loc('count')] = 0
        x_values = x_values.values
        latest_data = latest_data.reshape(1, n_steps, x_values.shape[1])

        #预测与反归一化
        predicted = self.loaded_model.predicted(latest_data)
        predicted_counts = self.scaler.inverse_transform(predicted)#反归一化
        predicted_counts[predicted_counts<0] = 0 #修正负数

        hourly_trend = predicted_counts[0].astype(int).tolist()
        print(hourly_trend)

if __name__ == '__main__':
    nn = NN()
    nn.get_hourly_trend()